The role of Market-incentive environmental regulation (MIER) within the framework of environmental governance is patently evident. While extant literature lauds the advantageous outcomes attributed to the environmental protection tax (EPT) which as a representative of MIER, our empirical inquiry presents a contrasting narrative. By employing the sophisticated Difference-in-Difference-in-Difference (DDD) methodology and utilizing data from A-share listed firms in Shanghai and Shenzhen from 2015-2022, our investigation reveals a significant decrease in firms’ total factor productivity (TFP) following the implementation of EPT. Our core assertion is fortified through the discernment of two plausible mechanisms, namely, the production downsizing effect and the production capital crowding-out effect. Building upon this revelation, we delve into the nuanced pathways through which firms can strategically mitigate the impacts of EPT, encompassing the enhancement of human capital, amplification of research and development (R&D) investments, and fortification of overall firm resilience. Heterogeneity analysis discloses a notably heightened impact of EPT on TFP of state-owned enterprises (SOEs), larger enterprises and enterprises located in eastern regions. Ultimately, an approximately cost-benefit analysis conclusively demonstrates that the benefits derived from EPT far surpass the costs incurred by the concomitant industrial output reduction, which further illustrates the rationale for the implementation of EPT.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.

Developing countries are taking advantage of FinTech tools to provide more people with convenient access to financial market investment through digital wealth management. Using COVID-19 as an uncertainty shock, we examine whether and how digital wealth management affects the resilience of consumption to shocks based on a unique micro dataset provided by a leading Big Tech platform, Alipay in China. We find that digital wealth management mitigates the response of consumption to uncertainty shocks: residents who participate in digital wealth management, especially in risky asset investments, have a lower reduction in consumption. Importantly, digital wealth management helps improve financial inclusion, with a more pronounced mitigation effect among residents with lower-level wealth, living in less developed areas, and those with lower-level conventional finance accessibility. The mitigation effect works through the wealth channel: those who allocate a larger proportion of risky assets in their portfolio and obtain a higher realized return show more resilience of consumption to negative shocks. We also find that digital wealth management substitutes for conventional bank credit but serves as a complement to FinTech credit in smoothing consumption during uncertainty shocks. Digital wealth management provides a crucial way to improve financial inclusion and the resilience of consumption to shocks.

There is substantial evidence indicating that enterprises are negatively impacted by climate risk, with the most direct effects typically occurring in financial domains. This study examines A-share listed companies from 2007 to 2023, employing text analysis to develop the firm-level climate risk indicator and investigate the influence on corporate financial risk. The results show a significant positive correlation between climate risk and financial risk at the firm level. Mechanism analysis shows that the negative impact of climate risk on corporate financial condition is mainly achieved through three paths: increasing financial constraints, reducing inventory reserves, and increasing the degree of maturity mismatch. To address potential endogeneity, this study applies instrumental variable tests, propensity score matching, and a quasi-natural experiment based on the Paris Agreement. Additional tests indicate that reducing the degree of information asymmetry and improving corporate ESG performance can alleviate the negative impact of climate risk on corporate financial conditions. This relationship is more pronounced in high-carbon emission industries. In conclusion, this research deepens the understanding of the link between climate risk and corporate financial risk, providing a new micro perspective for risk management, proactive governance transformation, and the mitigation of financial challenges faced by enterprises.

We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.

With fintech growth and smartphone adoption, e-wallets, which enable instant transactions while offering cash management products with financial returns, have become increasingly prevalent. Using a unique dataset from Alipay, the world’s largest e-wallet provider, we find that holdings in Yu’EBao—an investment product usable for payments—are less affected by interest rate changes than similar assets without payment functions. This effect is stronger for users who depend on Yu’EBao for daily spending, during peak payment periods, or among less experienced investors. Our findings show that Yu’EBao reduces retail fund flow to riskier assets by 7.7% for every one-percentage-point interest rate cut, dampening monetary policy transmission through the portfolio rebalancing channel.

This study shows how price limit rules, intended to stabilize markets, inadvertently distort price dynamics by fostering strategic speculation. Through a dynamic rational expectations model, we demonstrate that price limits induce post limit-up price jumps by impeding full information incorporation, enabling speculators to artificially push prices to upper bounds and exploit uninformed traders. The model predicts two distinct patterns: (1) stocks closing at price limits exhibit positive overnight returns followed by long-term reversals, and (2) stocks retreating from upper bounds suffer sharp reversals with partial recovery. Empirical analysis confirms these predictions. A natural experiment from China’s 2020 GEM reform —- which widened the price limit -— further provides causal evidence that relaxed limits mitigate speculative distortions.

This study examines the economic consequences of China’s Three-Red-Lines policy—introduced in 2021 to cap real estate developers’ leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance, with privately-owned developers disproportionately affected relative to state-owned firms. Using granular project-level data, we document significant drops in sales and a demand shift from private to state-owned developers. The policy also reduced local governments’ land sale revenues, prompting greater reliance on hidden local government financing vehicles for land purchases. The policy induced broad structural changes in China’s housing and land markets.

We utilize the Bayesian model scan approach to examine the best performing models in a set of 15 factors discovered in the literature, plus principal components (PCs) of anomalies unexplained by the initial factors in the Chinese A-share market. The Bayesian comparison of approximately eight million models shows that HML, MOM, IA, EG, PEAD, SMB, VMG,PMO, plus the four PCs, PC1, PC6, PC7, PC8 are the best supported specification in terms of marginal likelihoods and posterior model probabilities. We also find that the best model outperforms existing factor models in terms of pricing tests and out-of-sample Sharpe ratio.

This study explores the price drivers of Chinese Emissions Allowances (CEAs) in the early stage of China’s national carbon market. Using daily time series data from July 2021 to July 2023, we find limited influence from conventional drivers, including energy prices and economic factors. Instead, national power generation emerges as a significant driver. These are primarily due to the distinct institutional features of China’s national carbon market, notably its rate-based system and sectoral coverage. Moreover, the study uncovers cumulative abnormal volatility in CEA prices ranging from 12% to 20% around the end of the first compliance cycle, reflecting sentiments about the policy design and participants’ limited understanding about carbon trading. Our results extend previous literature regarding carbon pricing determinants by highlighting China’s unique carbon market design, comparing it with the traditional cap-and-trade programs, and offering valuable insights for tailored market-based policies in developing countries.